def test_barnes_hut_angle(): # When Barnes-Hut's angle=0 this corresponds to the exact method. angle = 0.0 perplexity = 10 n_samples = 100 for n_components in [2, 3]: n_features = 5 degrees_of_freedom = float(n_components - 1.0) random_state = check_random_state(0) distances = random_state.randn(n_samples, n_features) distances = distances.astype(np.float32) distances = distances.dot(distances.T) np.fill_diagonal(distances, 0.0) params = random_state.randn(n_samples, n_components) P = _joint_probabilities(distances, perplexity, False) kl, gradex = _kl_divergence(params, P, degrees_of_freedom, n_samples, n_components) k = n_samples - 1 bt = BallTree(distances) distances_nn, neighbors_nn = bt.query(distances, k=k + 1) neighbors_nn = neighbors_nn[:, 1:] Pbh = _joint_probabilities_nn(distances, neighbors_nn, perplexity, False) kl, gradbh = _kl_divergence_bh(params, Pbh, neighbors_nn, degrees_of_freedom, n_samples, n_components, angle=angle, skip_num_points=0, verbose=False) assert_array_almost_equal(Pbh, P, decimal=5) assert_array_almost_equal(gradex, gradbh, decimal=5)
def test_barnes_hut_angle(): # When Barnes-Hut's angle=0 this corresponds to the exact method. angle = 0.0 perplexity = 10 n_samples = 100 for n_components in [2, 3]: n_features = 5 degrees_of_freedom = float(n_components - 1.0) random_state = check_random_state(0) distances = random_state.randn(n_samples, n_features) distances = distances.astype(np.float32) distances = distances.dot(distances.T) np.fill_diagonal(distances, 0.0) params = random_state.randn(n_samples, n_components) P = _joint_probabilities(distances, perplexity, False) kl, gradex = _kl_divergence(params, P, degrees_of_freedom, n_samples, n_components) k = n_samples - 1 bt = BallTree(distances) distances_nn, neighbors_nn = bt.query(distances, k=k + 1) neighbors_nn = neighbors_nn[:, 1:] Pbh = _joint_probabilities_nn(distances, neighbors_nn, perplexity, False) kl, gradbh = _kl_divergence_bh(params, Pbh, neighbors_nn, degrees_of_freedom, n_samples, n_components, angle=angle, skip_num_points=0, verbose=False) assert_array_almost_equal(Pbh, P, decimal=5) assert_array_almost_equal(gradex, gradbh, decimal=5)
def tsneError_bh_full(params, P, degrees_of_freedom): X_embedded = params # params.reshape(n_samples, n_components) num_instances = X_embedded.shape[0] num_trees = X_embedded.shape[1] res = t_sne._kl_divergence_bh(params, P, degrees_of_freedom, num_instances, num_trees) return (res[0])
def test_barnes_hut_angle(): # When Barnes-Hut's angle=0 this corresponds to the exact method. angle = 0.0 perplexity = 10 n_samples = 100 for n_components in [2, 3]: n_features = 5 degrees_of_freedom = float(n_components - 1.0) random_state = check_random_state(0) data = random_state.randn(n_samples, n_features) distances = pairwise_distances(data) params = random_state.randn(n_samples, n_components) P = _joint_probabilities(distances, perplexity, verbose=0) kl_exact, grad_exact = _kl_divergence(params, P, degrees_of_freedom, n_samples, n_components) n_neighbors = n_samples - 1 distances_csr = NearestNeighbors().fit(data).kneighbors_graph( n_neighbors=n_neighbors, mode='distance') P_bh = _joint_probabilities_nn(distances_csr, perplexity, verbose=0) kl_bh, grad_bh = _kl_divergence_bh(params, P_bh, degrees_of_freedom, n_samples, n_components, angle=angle, skip_num_points=0, verbose=0) P = squareform(P) P_bh = P_bh.toarray() assert_array_almost_equal(P_bh, P, decimal=5) assert_almost_equal(kl_exact, kl_bh, decimal=3)
def test_barnes_hut_angle(): # When Barnes-Hut's angle=0 this corresponds to the exact method. angle = 0.0 perplexity = 10 n_samples = 100 for n_components in [2, 3]: n_features = 5 degrees_of_freedom = float(n_components - 1.0) random_state = check_random_state(0) distances = random_state.randn(n_samples, n_features) distances = distances.astype(np.float32) distances = abs(distances.dot(distances.T)) np.fill_diagonal(distances, 0.0) params = random_state.randn(n_samples, n_components) P = _joint_probabilities(distances, perplexity, verbose=0) kl_exact, grad_exact = _kl_divergence(params, P, degrees_of_freedom, n_samples, n_components) k = n_samples - 1 bt = BallTree(distances) distances_nn, neighbors_nn = bt.query(distances, k=k + 1) neighbors_nn = neighbors_nn[:, 1:] distances_nn = np.array( [distances[i, neighbors_nn[i]] for i in range(n_samples)]) assert np.all(distances[0, neighbors_nn[0]] == distances_nn[0]),\ abs(distances[0, neighbors_nn[0]] - distances_nn[0]) P_bh = _joint_probabilities_nn(distances_nn, neighbors_nn, perplexity, verbose=0) kl_bh, grad_bh = _kl_divergence_bh(params, P_bh, degrees_of_freedom, n_samples, n_components, angle=angle, skip_num_points=0, verbose=0) P = squareform(P) P_bh = P_bh.toarray() assert_array_almost_equal(P_bh, P, decimal=5) assert_almost_equal(kl_exact, kl_bh, decimal=3)
def test_barnes_hut_angle(): # When Barnes-Hut's angle=0 this corresponds to the exact method. angle = 0.0 perplexity = 10 n_samples = 100 for n_components in [2, 3]: n_features = 5 degrees_of_freedom = float(n_components - 1.0) random_state = check_random_state(0) distances = random_state.randn(n_samples, n_features) distances = distances.astype(np.float32) distances = abs(distances.dot(distances.T)) np.fill_diagonal(distances, 0.0) params = random_state.randn(n_samples, n_components) P = _joint_probabilities(distances, perplexity, verbose=0) kl_exact, grad_exact = _kl_divergence(params, P, degrees_of_freedom, n_samples, n_components) k = n_samples - 1 bt = BallTree(distances) distances_nn, neighbors_nn = bt.query(distances, k=k + 1) neighbors_nn = neighbors_nn[:, 1:] distances_nn = np.array([distances[i, neighbors_nn[i]] for i in range(n_samples)]) assert np.all(distances[0, neighbors_nn[0]] == distances_nn[0]),\ abs(distances[0, neighbors_nn[0]] - distances_nn[0]) P_bh = _joint_probabilities_nn(distances_nn, neighbors_nn, perplexity, verbose=0) kl_bh, grad_bh = _kl_divergence_bh(params, P_bh, degrees_of_freedom, n_samples, n_components, angle=angle, skip_num_points=0, verbose=0) P = squareform(P) P_bh = P_bh.toarray() assert_array_almost_equal(P_bh, P, decimal=5) assert_almost_equal(kl_exact, kl_bh, decimal=3)